Cheng-Yu Tsai

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The Clinical Document Architecture, introduced by Health Level Seven, is a XML-based standard intending to specify the encoding, structure, and semantics of clinical documents for exchange. Since the clinical document is in XML form, its authenticity and integrity could be guaranteed by the use of the XML signature published by W3C. While a clinical(More)
The diagnosis of intraductal proliferation poses a big challenge to pathologists or clinical surgeons in examinations of breast carcinoma. Clinically, there are three types of intraductal breast lesions including the usual ductal hyperplasia (UDH), atypical hyperplasia (ADH), and ductal carcinoma in situ (DCIS) for this examination. This study aims to(More)
In this work we aim to discover high quality speech features and linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using the metrics and corpora proposed in the Zero Resource Speech Challenge organized at Interspeech 2015. A Multi-layered Acoustic Tokenizer (MAT) was proposed for automatic discovery(More)
The application of GPS is growing fast recently. Not only in military and science purposes, but also in civil use, GPS plays an important role in many electronic systems. For example, the electronic navigation of automobile, the electronic map of PDA, etc. To deploy a research on this topic, we advise a low cost automobile GPS guidance system controlled by(More)
The study investigated the 2011 TPGA Ever Rich Championship – North Bay Open spectators’ on-the-site spectating motivations, experiences, and satisfactions. The research was conducted on a convenience sample of the on-the-spot spectators at the North Bay Golf and Country Club. A total of 200 questionnaires were distributed, of which 185 valid questionnaires(More)
This paper summarizes the work done by the authors for the Zero Resource Speech Challenge organized in the technical program of Interspeech 2015. The goal of the challenge is to discover linguistic units directly from unlabeled speech data. The Multi-layered Acoustic Tokenizer (MAT) proposed in this work automatically discovers multiple sets of acoustic(More)